Efficient Exploration of Long Data Series: A Data Event-driven HMI Concept

Bertram Wortelen; Viviane Herdel; Oliver Pfeiffer; Marie Christin Harre; Marcel Saager; Mathias Lanezki

doi:10.1007/978-3-030-50732-9_64

Efficient Exploration of Long Data Series: A Data Event-driven HMI Concept

Bertram Wortelen
, Viviane Herdel
, Oliver Pfeiffer
, Marie Christin Harre
, Marcel Saager
, Mathias Lanezki

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Today’s easy access to data, low cost sensors and data transmission infrastructure leads to an abundance of data about complex systems in many domains like industrial process control, network intrusion detection or maritime surveillance. Analyzing this data can take a lot of effort and often cannot be fully automated. As it is hard to fully automate such analysis tasks, we present an HMI framework that supports an analyst in exploring and navigating through multiple time series of data. It is a semi-automatic approach that uses algorithms for automatically labelling low-level events in the data, but leaves the task of evaluation and interpretation to the human operator. These events are highlighted on specific time bars in the HMI framework. It enables the analyst to 1) summarize the main features of the data series, 2) filter it depending on the analysis objective, 3) identify and prioritize relevant section in the data and 4) directly jump to these sections. We present the theoretical concept of the HMI framework and demonstrate it on a process control application for hybrid energy systems.

Original language	English
Title of host publication	HCI International 2020 - Posters - 22nd International Conference, HCII 2020, Proceedings
Editors	Constantine Stephanidis, Margherita Antona
Publisher	Springer
Pages	495-503
Number of pages	9
ISBN (Print)	9783030507312
DOIs	https://doi.org/10.1007/978-3-030-50732-9_64
State	Published - 1 Jan 2020
Externally published	Yes
Event	22nd International Conference on Human-Computer Interaction, HCII 2020 - Copenhagen, Denmark Duration: 19 Jul 2020 → 24 Jul 2020

Publication series

Name	Communications in Computer and Information Science
Volume	1226 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	22nd International Conference on Human-Computer Interaction, HCII 2020
Country/Territory	Denmark
City	Copenhagen
Period	19/07/20 → 24/07/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Data exploration
Data visualization
Event detection
System monitoring
Time series

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-030-50732-9_64

Cite this

Wortelen, B., Herdel, V., Pfeiffer, O., Harre, M. C., Saager, M., & Lanezki, M. (2020). Efficient Exploration of Long Data Series: A Data Event-driven HMI Concept. In C. Stephanidis, & M. Antona (Eds.), HCI International 2020 - Posters - 22nd International Conference, HCII 2020, Proceedings (pp. 495-503). (Communications in Computer and Information Science; Vol. 1226 CCIS). Springer. https://doi.org/10.1007/978-3-030-50732-9_64

@inproceedings{7ccc639bc1044618a3e81ce75c733d46,

title = "Efficient Exploration of Long Data Series: A Data Event-driven HMI Concept",

abstract = "Today{\textquoteright}s easy access to data, low cost sensors and data transmission infrastructure leads to an abundance of data about complex systems in many domains like industrial process control, network intrusion detection or maritime surveillance. Analyzing this data can take a lot of effort and often cannot be fully automated. As it is hard to fully automate such analysis tasks, we present an HMI framework that supports an analyst in exploring and navigating through multiple time series of data. It is a semi-automatic approach that uses algorithms for automatically labelling low-level events in the data, but leaves the task of evaluation and interpretation to the human operator. These events are highlighted on specific time bars in the HMI framework. It enables the analyst to 1) summarize the main features of the data series, 2) filter it depending on the analysis objective, 3) identify and prioritize relevant section in the data and 4) directly jump to these sections. We present the theoretical concept of the HMI framework and demonstrate it on a process control application for hybrid energy systems.",

keywords = "Data exploration, Data visualization, Event detection, System monitoring, Time series",

author = "Bertram Wortelen and Viviane Herdel and Oliver Pfeiffer and Harre, \{Marie Christin\} and Marcel Saager and Mathias Lanezki",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 22nd International Conference on Human-Computer Interaction, HCII 2020 ; Conference date: 19-07-2020 Through 24-07-2020",

year = "2020",

month = jan,

day = "1",

doi = "10.1007/978-3-030-50732-9\_64",

language = "English",

isbn = "9783030507312",

series = "Communications in Computer and Information Science",

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pages = "495--503",

editor = "Constantine Stephanidis and Margherita Antona",

booktitle = "HCI International 2020 - Posters - 22nd International Conference, HCII 2020, Proceedings",

address = "Germany",

}

Wortelen, B, Herdel, V, Pfeiffer, O, Harre, MC, Saager, M & Lanezki, M 2020, Efficient Exploration of Long Data Series: A Data Event-driven HMI Concept. in C Stephanidis & M Antona (eds), HCI International 2020 - Posters - 22nd International Conference, HCII 2020, Proceedings. Communications in Computer and Information Science, vol. 1226 CCIS, Springer, pp. 495-503, 22nd International Conference on Human-Computer Interaction, HCII 2020, Copenhagen, Denmark, 19/07/20. https://doi.org/10.1007/978-3-030-50732-9_64

Efficient Exploration of Long Data Series: A Data Event-driven HMI Concept. / Wortelen, Bertram; Herdel, Viviane; Pfeiffer, Oliver et al.
HCI International 2020 - Posters - 22nd International Conference, HCII 2020, Proceedings. ed. / Constantine Stephanidis; Margherita Antona. Springer, 2020. p. 495-503 (Communications in Computer and Information Science; Vol. 1226 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Efficient Exploration of Long Data Series

T2 - 22nd International Conference on Human-Computer Interaction, HCII 2020

AU - Wortelen, Bertram

AU - Herdel, Viviane

AU - Pfeiffer, Oliver

AU - Harre, Marie Christin

AU - Saager, Marcel

AU - Lanezki, Mathias

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Today’s easy access to data, low cost sensors and data transmission infrastructure leads to an abundance of data about complex systems in many domains like industrial process control, network intrusion detection or maritime surveillance. Analyzing this data can take a lot of effort and often cannot be fully automated. As it is hard to fully automate such analysis tasks, we present an HMI framework that supports an analyst in exploring and navigating through multiple time series of data. It is a semi-automatic approach that uses algorithms for automatically labelling low-level events in the data, but leaves the task of evaluation and interpretation to the human operator. These events are highlighted on specific time bars in the HMI framework. It enables the analyst to 1) summarize the main features of the data series, 2) filter it depending on the analysis objective, 3) identify and prioritize relevant section in the data and 4) directly jump to these sections. We present the theoretical concept of the HMI framework and demonstrate it on a process control application for hybrid energy systems.

AB - Today’s easy access to data, low cost sensors and data transmission infrastructure leads to an abundance of data about complex systems in many domains like industrial process control, network intrusion detection or maritime surveillance. Analyzing this data can take a lot of effort and often cannot be fully automated. As it is hard to fully automate such analysis tasks, we present an HMI framework that supports an analyst in exploring and navigating through multiple time series of data. It is a semi-automatic approach that uses algorithms for automatically labelling low-level events in the data, but leaves the task of evaluation and interpretation to the human operator. These events are highlighted on specific time bars in the HMI framework. It enables the analyst to 1) summarize the main features of the data series, 2) filter it depending on the analysis objective, 3) identify and prioritize relevant section in the data and 4) directly jump to these sections. We present the theoretical concept of the HMI framework and demonstrate it on a process control application for hybrid energy systems.

KW - Data exploration

KW - Data visualization

KW - Event detection

KW - System monitoring

KW - Time series

UR - https://www.scopus.com/pages/publications/85088748370

U2 - 10.1007/978-3-030-50732-9_64

DO - 10.1007/978-3-030-50732-9_64

M3 - Conference contribution

AN - SCOPUS:85088748370

SN - 9783030507312

T3 - Communications in Computer and Information Science

SP - 495

EP - 503

BT - HCI International 2020 - Posters - 22nd International Conference, HCII 2020, Proceedings

A2 - Stephanidis, Constantine

A2 - Antona, Margherita

PB - Springer

Y2 - 19 July 2020 through 24 July 2020

ER -

Wortelen B, Herdel V, Pfeiffer O, Harre MC, Saager M, Lanezki M. Efficient Exploration of Long Data Series: A Data Event-driven HMI Concept. In Stephanidis C, Antona M, editors, HCI International 2020 - Posters - 22nd International Conference, HCII 2020, Proceedings. Springer. 2020. p. 495-503. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-50732-9_64

Efficient Exploration of Long Data Series: A Data Event-driven HMI Concept

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this